BaMBA 11 (2017)


BaMBA is a one-day meeting aimed at exploring the role of mathematics in biology in an informal atmosphere. Going beyond traditional applied mathematics, the topics include applications of algebraic, topological, statistical and computational methods. Our goal is to encourage dialogue between researchers and students from different disciplines in an atmosphere that promotes the open exchange of ideas and viewpoints. We expect a day full of enticing discussions!

Participation in BaMBA is free and open to everyone, but registration is required. Attendance is limited to 200. The registration deadline is Monday November 13, 2017. Undergraduates, graduates, and postdocs involved in mathematical and computational investigations of biological systems are invited to submit an abstract for a poster presentation.


Genentech Hall
600 16th St, San Francisco, CA 94158


09:00-09:30 Register; coffee and pastries

09:30-10:30 Marina SirotaUC San Francisco : Drug Discovery in the Era of Precision Medicine

10:30-11:00 Coffee Break

11:00-12:00 Tim Lewis, UC Davis : Limb Coordination in Crustacean Swimming: Neural Mechanisms and Mechanical Implications

12:00-01:00 Lunch

01:00-02:00 Josh Stuart, UC Santa Cruz : N-of-1 networks to personalized cancer treatment

02:00-02:30 Break

02:30-03:30 Polly FordyceStanford University : Using microfluidics to understand how proteins find and bind their cellular targets

03:30-04:30 Nataša JonoskaUniversity of South Florida : Patterns in complex chromosome rearrangements

04:30-06:00 Poster Session and Reception:

All speaker abstracts and talk titles will be posted as they become available.
Marina Sirota
Drug Discovery in the Era of Precision Medicine
The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. The development and availability of large-scale genomic, transcriptomic, and other molecular profiling technologies and publically available databases, in combination with the deployment of the network concept of drug targets and the power of phenotypic screening, provide an unprecedented opportunity to advance rational drug repositioning and data-driven development of drug combinations based on the ability of single or multiple therapeutic agents to perturb entire molecular networks away from disease states in cell-based and animal models. Genomic and transcriptomic technologies allow us to extract large amounts of data from patient samples, elucidating previously unknown factors involved in disease, which could lead to identifying new therapeutic strategies. Such resources have been developed and exist for cancer research, including The Cancer Genome Atlas (TCGA), Oncomine and COSMIC, and have been instrumental in identification of novel biomarkers and drug targets in cancer and other diseases. We have previously developed a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrated its efficacy both in vitro and in vivo using mouse xenograft models. We also applied our computational approach to discover new drug therapies for inflammatory bowel disease (IBD) in silico and validated the prediction in animal models. The computational method provides a systematic approach for repositioning established drugs to treat a wide range of human diseases including dermatologic conditions such as dermatomyocitis and basal cell carcinoma. In this talk, I will discuss the computational methods that we have developed and applied across extensive molecular datasets in order to speed up the process of drug discovery as well as touch on new clinical datasets that we are incorporating in the pipeline.

Tim Lewis
Limb Coordination in Crustacean Swimming: Neural Mechanisms and Mechanical Implications
Despite the general belief that neural circuits have evolved to optimize behavior, few studies have clearly identified the neural mechanisms underlying optimal behavior. The distinct limb coordination in long-tailed crustacean swimming and the relative simplicity of the neural coordinating circuit have allowed us to show that the interlimb coordination in crustacean swimming is biomechanically optimal and how the structure of underlying neural circuit robustly gives rise to this coordination. Specifically, we use a computational fluid dynamics model to demonstrate that the crustacean stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers. We then use coupled oscillator theory to show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Our result provide a concrete example of how an optimal behavior arises from the anatomical structure of a neural circuit. Furthermore, they suggest that the connectivity of the neural circuit underlying limb coordination during crustacean swimming may be a consequence of natural selection in favor of more effective and efficient swimming.

Josh Stuart
N-of-1 networks to personalized cancer treatment
Cancers come in several forms according to the organ and tissue of origin, the type of mutagen and the impacted genetic pathways that contribute to oncogenic progression. Pan-Cancer analyses across multiple types of cancers, using multiple types of omics data, have identified molecular-based subtypes of clinical importance. Even so, patients may not respond to the usual treatment regime and carry their own unique alterations. I will discuss network integration strategies for building patient-specific networks to model the aberrant wiring in a single person’s tumor. The goal is to then strategies treatment for the person based on critical nodes in the uncovered network.

Polly Fordyce
Using microfluidics to understand how proteins find and bind their cellular targets
Personalized medicine promises to revolutionize clinical care by using information about genomic variation to predict an individual’s propensity to develop disease as well as to prescribe those therapeutics most likely to work.  However, realizing this promise requires the ability to quantitatively predict how genomic mutations affect protein function.  To date, we largely lack quantitative models that can predict how particular mutations affect molecular structure and function; as a result, the vast majority of identified human variants are currently of unknown significance.  To acquire the data necessary to develop these models, we have built several microfluidic platforms that allow quantitative, biophysical measurements of thousands of molecular interactions in parallel.  By combining iterative cycles of computational modeling with experimental measurements, we hope to unravel the mystery of how proteins find their molecular partners in the cell and improve mechanistic predictions of how mutations affect these interactions.

Nataša Jonoska
Patterns in complex chromosome rearrangements
RNA-guided genome editing is one of the breakthroughs of 21st century science, yet many similar processes remain undiscovered. Certain species of ciliates, such as Oxytricha trifallax, use RNA-guided genome editing in its routine development after conjugation and dramatically rewrite their genome. These species undergo hundreds of thousands of programmed DNA rearrangements, more than any other known organism, and are used as a model to study template-guided genome rearrangements. We investigate the genome-wide scrambled gene architectures that describe all precursor-product relationships in Oxytricha trifallax, the first completely sequenced scrambled genome. The situations are represented by words, graphs and chord diagrams to identify the common features. We show that five general, recurrent patterns in the sets of scrambled micronuclear precursor pieces can describe over 80% of Oxytricha’s scrambled genes. Moreover, we find that iterating patterns of alternating odd-even segments up to four times can describe over 98% of the scrambled precursor loci. Recurrence of these highly structured patterns within scrambled genes presumably reflects frequent evolutionary events that gave rise to thousands of scrambled germline genome.